Corner rounding method of opc pattern based on deep learning, and opc method and mask manufacturing method including the corner rounding method

ABSTRACT

The inventive concept provides a corner rounding method of a deep learning-based optical proximity correction (OPC) pattern by which patterning reliability may be ensured, and an OPC method and a mask manufacturing including the corner rounding method. The corner rounding method of a deep learning-based OPC pattern includes: obtaining a contour of a photoresist (PR) pattern or an etching pattern on a wafer; obtaining a square layout of the PR pattern or the etching pattern corresponding to the contour; generating a transform model through deep learning with the square layout and the contour; and obtaining a rounded layout target with respect to a square layout target by using the transform model.

CROSS-REFERENCE TO RELATED APPLICATION

This application is based on and claims priority under 35 U.S.C. § 119to Korean Patent Application No. 10-2022-0028962, filed on Mar. 7, 2022,in the Korean Intellectual Property Office, the disclosure of which isincorporated by reference herein in its entirety.

BACKGROUND

The inventive concepts relate to a mask manufacturing method, and moreparticularly, to an optical proximity correction (OPC) method and a maskmanufacturing method.

In a semiconductor process, a photolithography process using a mask maybe performed to form a pattern on a semiconductor substrate, such as awafer. The mask may be called a pattern-transferred body having apattern shape of an opaque material, which is formed on a base layermaterial. To manufacture this mask, a layout of a required or targetpattern is first designed, and then OPCed layout data obtained throughOPC is transmitted as mask tape-out (MTO) design data. Thereafter, basedon the MTO design data, mask data preparation (MDP) may be performed,and an exposure process (and the like) may be performed on a substratefor the mask.

SUMMARY

The inventive concepts provide a corner rounding method of a deeplearning-based optical proximity correction (OPC) pattern by whichpatterning reliability may be ensured, and an OPC method and a maskmanufacturing method including the corner rounding method.

In addition, problems to be solved by the technical idea of theinventive concepts are not limited to the problems described above, andthe other problems could be clearly understood to those of ordinaryskill in the art from the description below.

According to an aspect of the inventive concepts, there is provided amethod of training a transform model to perform a corner rounding methodof a deep learning-based optical proximity correction (OPC) pattern, thecorner rounding method including: obtaining a contour of at least one ofa photoresist (PR) pattern or an etching pattern on a wafer; obtaining asquare layout of the at least one PR pattern or etching patterncorresponding to the contour; and generating a transform model throughdeep learning with the square layout and the contour such that thetransform model is configured to output a rounded layout target withrespect to a square layout target.

According to another aspect of the inventive concepts, there is providedan OPC method including: generating a retarget layout including straightedges; performing OPC on the retarget layout such that an OPCed layoutis generated; performing an optical rule check (ORC) on the OPCedlayout; determining whether there is a defect in the OPCed layout basedon a result of the ORC; determining the OPCed layout to be a final OPCedlayout based on the determination that there is no defect; and etching asemiconductor element based on the final OPCed layout, wherein at leastone of the retarget layout or the OPCed layout is generated based on acorner rounding of a deep learning-based OPC pattern.

According to another aspect of the inventive concepts, there is provideda mask manufacturing method including: performing an optical proximitycorrection (OPC) on the retarget layout such that an OPCed layout isgenerated; performing an optical rule check (ORC) on the OPCed layout;determining whether there is a defect in the OPCed layout based on aresult of the ORC; and determining the OPCed layout to be a final OPCedlayout based on the determination that there is no defect; transmittingan image of the final OPCed layout as mask tape-out (MTO) design data;preparing mask data based on the MTO design data; and exposing asubstrate to light, based on the mask data, wherein at least one of theretarget layout or the OPCed layout is generated based on a cornerrounding of a deep learning-based OPC pattern.

BRIEF DESCRIPTION OF THE DRAWINGS

Example embodiments of the inventive concepts will be more clearlyunderstood from the following detailed description taken in conjunctionwith the accompanying drawings in which:

FIG. 1 is a flowchart schematically illustrating an optical proximitycorrection (OPC) method including a corner rounding method of a deeplearning-based OPC pattern, according to at least one embodiment of theinventive concepts;

FIG. 2 is a conceptual diagram of describing a part to which the cornerrounding method is applied in the OPC method of FIG. 1 , among eightmajor steps to semiconductor fabrication;

FIG. 3 is a conceptual diagram of describing generating a transformmodel through deep learning using a generative adversarial network(GAN), which is used in the corner rounding method in the OPC method ofFIG. 1 ;

FIG. 4 is a block structural diagram particularly illustrating astructure of the GAN used in the corner rounding method in the OPCmethod of FIG. 1 ;

FIGS. 5A and 5B are flowcharts more particularly illustrating the cornerrounding method in an operation of generating a retarget layout and anoperation of generating an OPCed layout, respectively, in the OPC methodof FIG. 1 ;

FIGS. 6A and 6B are images illustrating a first contour and a firstsquare layout of FIG. 5A;

FIGS. 7A and 7B are flowcharts illustrating generating the retargetlayout in the operation of generating the retarget layout and generatingthe OPCed layout in the operation of generating the OPCed layout,respectively, in the OPC method of FIG. 1 ;

FIGS. 8A and 8B are images for describing Manhattanization on acurvilinear layout; and

FIG. 9 is a flowchart schematically illustrating a mask manufacturingmethod including a corner rounding method, according to at least oneembodiment of the inventive concepts.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Hereinafter, some example embodiments of the inventive concepts aredescribed in detail with reference to the accompanying drawings. Likereference numerals in the drawings denote like elements, and theirrepetitive descriptions are omitted.

FIG. 1 is a flowchart schematically illustrating an optical proximitycorrection (OPC) method including a corner rounding method of a deeplearning-based OPC pattern, according to at least one embodiment of theinventive concepts.

Referring to FIG. 1 , in the OPC method including the corner roundingmethod of a deep learning-based OPC pattern (hereinafter, simply “OPCmethod”) according to the present example, first, a retarget layout isgenerated in operation S110. The retarget layout may be generated by thecorner rounding method of the deep learning-based OPC pattern(hereinafter, simply “corner rounding method”). The corner roundingmethod is described in more detail with reference to FIGS. 5A and 5B. Inaddition, the generating the retarget layout by the corner roundingmethod is described in more detail with reference to FIG. 7A.

As a reference, to generally form a target pattern on a wafer substrate,a target layout of a pattern on a mask is first generated. Herein, thetarget pattern indicates a pattern formed by transferring the pattern onthe mask onto the wafer substrate through an exposure process, and theshape of the target pattern may be different from the shape of thepattern on the mask according to the characteristics of the exposureprocess. In addition, because the pattern on the mask may bereduction-projected and transferred onto the wafer substrate, thepattern on the mask may have a greater size than the target pattern onthe wafer substrate.

Along with pattern micro-fabrication, an optical proximity effect (OPE)(e.g., due to an influence between neighboring patterns) may occurduring the exposure process. To overcome the OPE, OPC for suppressingthe OPE from occurring (e.g., by correcting a target layout of a patternon a mask) may be performed. In addition, to ensure the reliability ofpatterning according to a corner rounding change in an exposure processand/or an etching process (e.g., before performing general OPC)generating a retarget layout (e.g., by correcting a target layout of apattern on a mask) may be performed in advance. The general OPC isdescribed in more detail in the description of operation S130 ofgenerating an OPCed layout through OPC.

With respect to the corner rounding change, an existing scheme ofanalyzing corner rounding of sampled patterns on a wafer substrate tocorrect a target layout based on a corner rounding difference may beemployed, but the reliability of such a scheme decreases becausecorrection values of sampled patterns are applied as approximate valuesto target layouts of various patterns. However, the OPC method accordingto the present example may generate a retarget layout by using thecorner rounding method, thereby improving the accuracy and reliabilityof patterning.

After generating the retarget layout, an OPCed layout is generatedthrough OPC in operation S130. Herein, the OPC may indicate the generalOPC. The OPC method may further include additional operations, such asperforming an optical rule check (ORC) in operation S150 and/or thelike.

The general OPC may be classified as at least one of a rule-based OPCand/or a simulation-based (or model-based) OPC. In some examples, themodel-based OPC may be faster and/or cheaper compared to the rule-basedOPC as only a measurement result of representative patterns is used inthe model-based OPC (e.g., without measuring all of bulk test patterns).

The general OPC may include a method of adding sub-lithographic features(or serifs) and/or sub-resolution assist features (SRAFs) (such asscattering bars) onto a corner of a pattern in addition to a shapechange in a layout of the pattern.

The performing the OPC may first include preparing basic data for theOPC. Herein, the basic data may include data of at least one of shapesof patterns of samples, positions of the patterns, types of measurements(e.g., on spaces, lines, and/or the like of the patterns), basicmeasurement values, and/or the like. In addition, the basic data mayinclude information about at least one of the thickness, the refractiveindex, the dielectric constant, and/or the like of a photoresist (PR)and/or may include a source map of an illumination system form. Ofcourse, the basic data is not limited to the data described above.

After preparing the basic data, an optical OPC model is generated. Thegenerating the optical OPC model may include improving (or optimizing) adefocus stand (DS) position, a best focus (BF) position, and/or the likein an exposure process. In addition, the generating the optical OPCmodel may include generating an optical image and/or the like,considering a diffraction phenomenon of light and/or a self-opticalstate of exposure equipment. Of course, the generating the optical OPCmodel is not limited to the description made above. For example, thegenerating the optical OPC model may include various informationassociated with optical phenomena in an exposure process.

After generating the optical OPC model, an OPC model for a PR isgenerated. The generating the OPC model for the PR may includegenerating (or optimizing) a threshold for the PR. Herein, the thresholdfor the PR indicates a threshold at which a chemical change occurs in anexposure process, and for example, the threshold may be given as theintensity of exposure light. The generating the OPC model for the PR mayfurther include selecting an appropriate model form from among aplurality of PR model forms.

The combination of the optical OPC model and the OPC model for the PRmay be generally referred to as the OPC model. The generating the OPCmodel may also be referred as training or learning the OPC model. Aftergenerating the OPC model, a simulation using the OPC model is performedto generate an OPCed layout. The OPC method according to the presentembodiment may further include, in addition to the general OPC,generating an OPCed layout based on the corner rounding method. Thegenerating the OPCed layout is described in more detail with referenceto FIG. 7B.

After generating the OPCed layout, an ORC is performed on the OPCedlayout in operation S150. The ORC may include, for example, a root meansquare (RMS) calculation on a critical dimension (CD) error, an edgeplacement error (EPE) calculation, a pinch error check, a bridge errorcheck, and/or the like. Of course, items inspected in the ORC are notlimited to the items described above.

After performing the ORC, it is determined, in operation S170, whetherthere is a defect. Herein, the defect may correspond to when an RMS of aCD error is greater than a set reference value, when an EPE is greaterthan a set reference value, when there is a pinch error, when there is abridge error, and/or the like. In addition, when there are other itemsin the ORC, corresponding items that deviate from reference values mayalso correspond to defects. According to at least one embodiment,operation S170 of determining whether there is a defect may be includedin operation S150 of performing the ORC.

If there is a defect (Yes), the OPC method proceeds back to operationS130 of generating an OPCed layout through OPC. Before reiteratingoperation S130 of generating an OPCed layout through OPC, a cause of thedefect may be analyzed, and the cause may be reflected in the OPC model.

If there is no defect (No), an OPCed layout, in which there is nodefect, is determined to be a final OPCed layout in operation S190. Thefinal OPCed layout may correspond to design data of a mask. Thereafter,the final OPCed layout may be transmitted to a mask manufacturing teamas mask tape-out (MTO) design data for manufacturing a mask. Forexample, in at least one embodiment, the final OPCed layout may betransmitted as instructions to semiconductor fabrication equipment and asemiconductor device may be manufactured based on the final OPCedlayout.

The OPC method according to the present embodiment may accuratelygenerate a retarget layout and/or an OPCed layout based on the cornerrounding method of a deep learning-based OPC pattern. Accordingly, theOPC method according to the present example may contribute tomanufacturing of a reliable mask and improvement of the accuracy andreliability of patterning by the mask.

FIG. 2 is a conceptual diagram of describing a part to which the cornerrounding method is applied in the OPC method of FIG. 1 , among eightmajor steps to semiconductor fabrication.

Referring to FIG. 2 , in general, a photo process may indicate a processof forming a PR pattern on a wafer substrate (e.g., through an exposureprocess using a mask), and a development process. In addition, anetching process may indicate a process of forming a pattern on the wafersubstrate (and/or on a material layer on the wafer substrate) using thePR pattern. In the etching process, process proximity correction (PPC)may be performed to compensate for an etch bias.

OPC may be performed in the photo process, and an image (and/or data) ofan OPCed layout may be obtained through the OPC. Accordingly, the photoprocess may include generating an OPCed layout through OPC,manufacturing a mask based on the OPCed layout, forming a PR pattern ona wafer substrate through an exposure process using the mask, and/or thelike.

The OPC method according to the present example may include the cornerrounding method of a deep learning-based OPC pattern. Deep learning maybe performed using, for example, a generative adversarial network (GAN),CNN (Convolution Neural Network), R-CNN (Region with Convolution NeuralNetwork), RPN (Region Proposal Network), RNN (Recurrent Neural Network),S-DNN (Stacking-based deep Neural Network), S-SDNN (State-Space DynamicNeural Network), Deconvolution Network, DBN (Deep Belief Network), RBM(Restricted Boltzmann Machine), Fully Convolutional Network, LSTM (LongShort-Term Memory) Network, Classification Network and BNN (BayesianNeural Network). Additionally (and/or alternatively), the deep learningmodel(s) may be trained based on at least one of various algorithms suchas regression, linear and/or logistic regression, random forest, asupport vector machine (SVM), and/or other types of models, such asstatistical clustering, Bayesian classification, decision trees,dimensionality reduction such as principal component analysis, expertsystems, and/or combinations thereof including ensembles such as randomforests. In at least one embodiment, in the corner rounding method ofthe OPC method according to the present example, an input image providedto a transform model may be an after develop inspection (ADI) targetimage, and an output image from the transform model may be a contourimage of a PR pattern on a wafer substrate. Herein, the contour image ofthe PR pattern may correspond to a rounded ADI target image. In thecorner rounding method of the PPC, an input image provided to thetransform model may be an after cleaning inspection (ACI) target image,and an output image from the transform model may be a contour image ofan etching pattern on a wafer substrate. Herein, the contour image ofthe etching pattern may correspond to a rounded ACI target image.Generating a transform model through deep learning using a GAN, and astructure of the GAN are described in more detail with respect to FIGS.3 and 4 .

FIG. 3 is a conceptual diagram of describing generating a transformmodel through deep learning using a GAN, which is used in the cornerrounding method in the OPC method of FIG. 1 .

Referring to FIG. 3 , in the corner rounding method in the OPC methodaccording to the present example, an input image and an output image maybe used as images for deep learning using the GAN. When simplydescribing the GAN, the GAN may include two sub-models as deeplearning-based generative algorithms That is, the GAN may include agenerator model and a discriminator model. The generator model maycorrespond to the transform model in the OPC method. The generator modelgenerates new examples, and the discriminator model determines whether agenerated example is real data or fake data generated by the generatormodel.

For example, the generator model may transform an input image togenerate an output image OPI corresponding to an image after OPC and/orPPC. For example, in the OPC, an input image IPI provided to thegenerator model may be an ADI image, and an output image OPI from thegenerator model may be a contour image of a PR pattern on a wafersubstrate. In addition, in the PPC, an input image IPI provided to thegenerator model may be an ACI image, and an output image OPI from thegenerator model may be a contour image of an etching pattern on a wafersubstrate.

An output image OPI generated by the generator model and a referenceimage RI may be input to the discriminator model. Herein, the referenceimage RI may correspond to a final image (e.g., which an output imageOPI is supposed to reach). For example, when an output image OPI may bea contour image of a PR pattern, the reference image RI may be a roundedADI target image corresponding to a PR pattern on a real wafersubstrate. In addition, when the output image OPI is a contour image ofan etching pattern, the reference image RI may be a rounded ACI targetimage corresponding to an etching pattern on a real wafer substrate. Thediscriminator model compares an output image OPI with the referenceimage RI and determines whether the output image OPI is a real image ora fake image generated by the generator model. In other words, thediscriminator model may determine that an output image OPI is a “real”image if the output image OPI is the same (e.g., within a preset (orotherwise determined) tolerance threshold) as the reference image RI,and determine that the output image OPI is a fake image if the outputimage OPI differs from the reference image RI beyond the preset (orotherwise determined) tolerance threshold.

Particularly, in FIG. 3 , when an ADI image as an input image IPI isinput to the generator model, the generator model generates an outputimage OPI, which is a contour image of a PR pattern. Thereafter, theoutput image OPI and a reference image RI are input to the discriminatormodel. Herein, the reference image RI may be a rounded ADI target imagecorresponding to a PR pattern on a real wafer substrate. In thereference image RI of FIG. 3 , the solid line indicates the rounded ADItarget image, and the dashed line indicates the contour image of the PRpattern of the output image OPI.

Thereafter, the discriminator model determines whether the output imageOPI is the same as the reference image RI. For example, thediscriminator model determines whether a contour image of a PR patterngenerated by the generator model is substantially the same as a roundedADI target image corresponding to a PR pattern on a real wafersubstrate. Thereafter, according to a result of the determination, thegenerator model and the discriminator model are continuously updated.When the discriminator model cannot discriminate the output image OPIfrom the reference image RI any more according to repeating thisprocedure described above, deep learning ends, and the generator modelat this time point may be adopted as a final transform model. When deeplearning ends, the discriminator model may be discarded.

FIG. 4 is a block structural diagram particularly illustrating astructure of the GAN used in the corner rounding method in the OPCmethod of FIG. 1 .

Referring to FIG. 4 , in the corner rounding method in the OPC methodaccording to the present embodiment, in order for the generator model ofthe GAN (e.g., a transform model) to generate a relatively accurateimage, features may be accurately extracted from input images. In someexample embodiments, convolution, as shown in FIG. 4 , may be performedto extract the features. Accordingly, in the corner rounding method inthe OPC method, the GAN may be a deep convolution GAN (DCGAN). Theconvolution is performed using a convolution filter and may includedown-sampling and up-sampling. In addition, for relatively accuratelearning, residual learning may be included between the down-samplingand the up-sampling. Through the residual learning, an optical effect ofa peripheral region may be reflected.

In the corner rounding method in the OPC method, a once moredown-sampled image down3 may be used for residual learning, thenup-sampled up1, concatenated with a previous image having undergone theresidual learning, and up-sampled (e.g., up2). In addition, in thecorner rounding method in the OPC method according to the presentembodiment, down-sampling may be performed twice (or more), and residuallearning may be performed with each image having undergone down-samplingtwice (or more).

In the OPC method, the GAN may include a plurality of down-sample layers(e.g., to have a structure of enabling a pixel correlation up to a fardistance). In some example embodiments, when an input image passesthrough a down-sample layer, the input image may be reduced (e.g., to ahalf size) at an output layer. However, because a reduced image stillimplies pattern information corresponding to the same width as the widthof the input image, information represented by one pixel may correspondto two times (or four times in an area concept) of that of the inputimage. As a result, even though kernels of the same size are used, akernel applied to an image having passed through a greater number ofdown-sample layers may represent a pixel correlation of a wider region.

In addition, with respect to residual learning, although a residualblock first structure is illustrated in FIG. 4 , a residual block laststructure may be used instead. For example, residual learning may beperformed after down-sampling in the residual block first structure,whereas down-sampling may be performed after residual learning in theresidual block last structure. Although image synthesis may be performedusing a concatenation layer structure, the image synthesis may beperformed using a sum-fusion layer structure. The concatenation layerstructure may have a twice larger structure in a channel direction, andthus, a kernel may also be larger, and a greater number of parametersare included. In addition, the sum-fusion layer structure may begenerated through an elementwise sum, and thus, an output result of asimilar performance may be obtained while maintaining a small-sizedkernel.

FIGS. 5A and 5B are flowcharts more particularly illustrating the cornerrounding method in operation S110 of generating a retarget layout andoperation S130 of generating an OPCed layout, respectively, in the OPCmethod of FIG. 1 .

Referring to FIG. 5A, in an OPC method, operation S110 of generating aretarget layout may include the corner rounding method. For example, inoperation S110 of generating a retarget layout, the corner roundingmethod may include the following operations. First, a first contour ofan etching pattern is obtained in operation S101. The etching patternmay indicate a pattern formed through an etching process using a PRpattern on a wafer substrate. The etching pattern may have cornersrounded by a corner rounding effect in the etching process. The firstcontour may be generated using, e.g., a mean value from a plurality ofscanning electron microscope (SEM) images of the etching pattern.

Next, a first square layout of the etching pattern is obtained inoperation S103. Herein, a square layout may indicate a layout includingonly square edges. Accordingly, the first square layout may include onlysquare edges. The first square layout may be obtained by performingManhattanization on the first contour. As a reference, several firstcontours may correspond to one first square layout. For example, incorrespondence to ACI of one square layout, various forms of contours ofthe etching pattern on the wafer substrate may be obtained.Manhattanization is described in more detail with reference to FIGS. 8Aand 8B.

Thereafter, a first transform model is generated through deep learningusing, e.g., a GAN in operation S105. In the deep learning, the firsttransform model may correspond to a generator model, a first squarelayout may correspond to an input image, and a first contour maycorrespond to an output image. Deep learning may be performed using aplurality of first square layouts and first contours corresponding tothe plurality of first square layouts, and a final generator model maybe generated as the first transform model. Herein, the first squarelayout may correspond to ACI, and the first contour may correspond torounded ACI.

After generating the first transform model, a first rounded layouttarget is obtained through the first transform model in operation S107.For example, when a first square layout target corresponding to a targetis input to the first transform model, the first transform model mayoutput a first contour (e.g., a first rounded layout target)corresponding to the first square layout target. Herein, the firstsquare layout target may indicate an ACI target of a square layout form,and the first rounded layout target may correspond to a rounded ACItarget in which corner rounding in an etching process is reflected.

The OPC method according to the present example may accurately obtain arounded ACI target in which corner rounding is reflected, by using thecorner rounding method of a deep learning-based OPC pattern, inoperation S110 of generating a retarget layout.

Referring to FIG. 5B, in the OPC method operation S130 of generating anOPCed layout may include the corner rounding method. Particularly, inoperation S130 of generating an OPCed layout, the corner rounding methodmay include the following operations. First, a second contour of a PRpattern is obtained in operation S121. The PR pattern may indicate a PRpattern formed through an exposure process on a PR layer on a wafersubstrate. The exposure process may include a development process. ThePR pattern may have corners rounded by a corner rounding effect in theexposure process. The second contour may be generated using a mean valuefrom a plurality of SEM images of the PR pattern.

Next, a second square layout of the PR pattern is obtained in operationS123. The second square layout may also include only square edges. Thesecond square layout may be obtained by performing Manhattanization onthe second contour. As a reference, several second contours maycorrespond to one second square layout. In other words, incorrespondence to ADI of one square layout, various forms of contours ofthe PR pattern on the wafer substrate may be obtained.

Thereafter, a second transform model is generated through deep learningusing, e.g., a GAN, in operation S125. In the deep learning, the secondtransform model may correspond to a generator model, the second squarelayout may correspond to an input image, and the second contour maycorrespond to an output image. Deep learning may be performed using aplurality of second square layouts and second contours corresponding tothe plurality of second square layouts, and a final generator model maybe generated as the second transform model. Herein, the second squarelayout may correspond to ADI, and the second contour may correspond torounded ADI.

After generating the second transform model, a second rounded layouttarget is obtained through the second transform model in operation S127.For example, when a second square layout target corresponding to atarget is input to the second transform model, the second transformmodel may output a second contour (e.g., a second rounded layout target)corresponding to the second square layout target. Herein, the secondsquare layout target may indicate an ADI target of a square layout form,and the second rounded layout target may correspond to a rounded ADItarget in which corner rounding in an exposure process is reflected.

The OPC method according to the present embodiment may accurately obtaina rounded ADI target in which corner rounding is reflected, by using thecorner rounding method of a deep learning-based OPC pattern in operationS130 of generating an OPCed layout.

FIGS. 6A and 6B are images illustrating the first contour and the firstsquare layout of FIG. 5A, wherein the thick solid line corresponds tothe first contour, and the thin solid line corresponds to the firstsquare layout.

Referring to FIGS. 6A and 6B, when first square layouts ACI1 and ACI2are input to the first transform model, first contours or first roundedlayouts R-ACI1 and R-ACI2 may be obtained. The first transform model maybe generated through, for example, deep learning using, e.g., a GAN.According to related art, when rule-based rounded layouts are generated,unlike a contour on a real wafer substrate, the rule-based roundedlayouts do not have a different curvature for each corner, and may alsohave a plurality of corner portions having a big difference from thecontour on the real wafer substrate. However, in the OPC methodaccording to the present example embodiments, a rounded layout may begenerated through a transform model, thereby accurately generating arounded layout substantially the same as a contour on a real wafersubstrate.

FIGS. 7A and 7B are flowcharts illustrating generating the retargetlayout in operation S110 of generating a retarget layout and generatingthe OPCed layout in operation S130 of generating an OPCed layout,respectively, in the OPC method of FIG. 1 .

Referring to FIG. 7A, in the OPC method, operation S110 of generating aretarget layout may include obtaining a first rounded layout targetthrough the corner rounding method and then generating a final retargetlayout through inverse correction and Manhattanization. Moreparticularly, in operation S110 of generating a retarget layout, acurvilinear ADI layout target may be generated as follows.

First, a curvilinear ADI layout target is generated through an inversecorrection in operation S111. The inverse correction may be achievedthrough deep learning using, e.g., a GAN. When simply describing theinverse correction, in the deep learning using a GAN, a forward modeland an inverse model opposite to the forward model may be generatedusing an ADI image as an input image and an ACI contour on a wafersubstrate as an output image. For example, the forward model maycorrespond to a model for transforming an ADI image into an ACI contourimage, and the inverse model may correspond to a model for inverselytransforming an ACI contour image into an ADI image.

When an ADI target image of a square layout form is input to the forwardmodel, the forward model outputs a curvilinear ACI target image.Thereafter, a corrected ADI target image may be generated through EPE.When a rounded ACI target image is input to the inverse model, theinverse model outputs a curvilinear ADI target image. Thereafter, an ADItarget may be generated by performing Manhattanization on thecurvilinear ADI target image.

In some example embodiments, the inverse model may use an ACI contourfrom the forward model instead of an ACI contour on a wafer substrate.For example, after generating the forward model through the methoddescribed above, forward ACI contour images respectively correspondingto ADI images of a square layout form may be generated by using theforward model, and the inverse model may be generated in deep learningusing a GAN by using the ADI images and the forward ACI contour images.Thereafter, a rounded ACI target image may be input to the inverse modelto output a curvilinear ADI target image.

As a reference, the terms ‘rounded’ and ‘curvilinear’ may be generallyused to substantially mean the same. However, when strictlydiscriminating, the term ‘rounded’ indicates a curved shape of a cornerportion, whereas the term ‘curvilinear’ indicates a curved shape of theentire layout.

After generating the curvilinear ADI layout target, a final retargetlayout may be generated through Manhattanization in operation S113. Asdescribed above, a retarget layout may include square edges.

Referring to FIG. 7B, in the OPC method, operation S130 of generating anOPCed layout may include obtaining a second rounded layout targetthrough the corner rounding method and then generating a final OPCedlayout through inverse correction. For example, operation S130 ofgenerating an OPCed layout includes generating a curvilinear OPCedlayout through inverse correction in operation S131. The inversecorrection in operation S130 of generating an OPCed layout may also beachieved through deep learning using, e.g., a GAN.

In operation S110 of generating a retarget layout, a curvilinear ACItarget is transformed into a curvilinear ADI target through inversecorrection, whereas, in operation S130 of generating an OPCed layout,the curvilinear ADI target is transformed into a curvilinear OPCedlayout through inverse correction. For example, in the deep learning,OPCed layouts may be used as input images, and curvilinear ADI imagesmay be used as output images. In addition, in the deep learning, theforward model and the inverse model may be generated. Therefore, when acurvilinear ADI target is input to the inverse model, a curvilinearOPCed layout may be output.

FIGS. 8A and 8B are images for describing Manhattanization on acurvilinear layout. FIG. 8A shows a curvilinear layout, and FIG. 8Bshows a square layout after Manhattanization.

Referring to FIGS. 8A and 8B, Manhattanization indicates fracturing apattern into segments and/or fracturing an edge of a pattern intostraight edges. For example, Manhattanization may indicate fracturing anedge of a pattern into straight edges vertical or horizontal to areference axis. For example, a curvilinear layout RL of FIG. 8A may betransformed into a square layout MRL including straight edges throughManhattanization. In general, Manhattanization may be performed by anautomation program. In the automation program, when setting informationabout a reference axis and size information about segments or straightedges and then inputting a curvilinear layout to the automation program,the automation program may automatically transform the curvilinearlayout into a square layout through Manhattanization.

FIG. 9 is a flowchart schematically illustrating a mask manufacturingmethod including a corner rounding method, according to an embodiment ofthe inventive concept.

Referring to FIG. 9 , in the mask manufacturing method including thecorner rounding method (hereinafter, simply “mask manufacturing method”)according to the present embodiment, first, a retarget layout isgenerated in operation S210. The retarget layout may be generated by thecorner rounding method. Herein, the corner rounding method may indicatea corner rounding method of a deep learning-based OPC pattern. Thecorner rounding method has been described above with reference to FIGS.5A and 5B. In addition, the generating the retarget layout by the cornerrounding method has been described above with reference to FIG. 7A.

After generating the retarget layout, an OPCed layout is generatedthrough OPC in operation S230. Herein, the OPC may indicate the generalOPC. The general OPC may include a method of adding sub-lithographicfeatures and/or SRAFs onto a corner of a pattern in addition to a shapechange in a layout of a pattern.

Performing OPC may include first preparing basic data for the OPC,generating an optical OPC model, generating an OPC model for a PR,and/or the like. A combination of the optical OPC model and the OPCmodel for the PR is generally called an OPC model, and after generatingthe OPC model, a simulation using the OPC model is performed to generatean OPCed layout. The mask manufacturing method according to the presentembodiment may further include, in addition to the general OPC,generating an OPCed layout based on the corner rounding method.

After generating the OPCed layout, an ORC is performed on the OPCedlayout in operation S250. The ORC may include, for example, RMScalculation on a CD error, an EPE calculation, a pinch error check, abridge error check, and/or the like. Of course, items inspected in theORC are not limited to the items described above.

After performing the ORC, it is determined in operation S270 whetherthere is a defect. For example, the defect may correspond to when an RMSof the CD error is greater than a set reference value, when an EPE isgreater than a set reference value, when there is a pinch error, whenthere is a bridge error, and/or the like. In addition, when there areother items in the ORC, cases where corresponding items deviate from areference may also correspond to defects.

If there is a defect (Yes), the mask manufacturing method proceeds tooperation S230 of generating an OPCed layout through OPC. Before thereiteration of operation S230 of generating an OPCed layout through OPC,a cause of the defect may be analyzed, and the cause may be reflected inthe OPC model.

If there is no defect (No), an OPCed layout, in which there is nodefect, is determined to be a final OPCed layout in operation S290.Operation S210 of generating a retarget layout to operation S290 ofdetermining a final OPCed layout may correspond to the OPC method, ofFIG. 1 , including a corner rounding method.

Thereafter, the final OPCed layout image is transmitted to a maskmanufacturing team as MTO design data in operation S292. In general, MTOmay indicate transmitting final mask data obtained by an OPC method tothe mask manufacturing team to request mask manufacturing. Therefore,the MTO design data may be substantially the same as data of the finalOPCed layout image obtained by the OPC method. The MTO design data mayhave a graphic data format used in electronic design automation (EDA)software and the like. For example, the MTO design data may have a dataformat, such as graphic data system II (GDS2) or open artwork systeminterchange standard (OASIS).

Thereafter, mask data preparation (MDP) is performed in operation S294.The MDP may include, for example, i) format transform called fracturing;ii) augmentation of a barcode for mechanical reading, a standard maskpattern for inspection, a job deck, and/or the like; iii) validation inan automatic and manual manners; and/or the like. Herein, the job deckmay indicate creating a text file related to arrangement information ofmulti-mask files, a reference dose, and a series of instructions relatedto an exposure speed and scheme and/or the like.

In addition, the format transform (e.g., fracturing) may indicate aprocess of fracturing the MTO design data for each region to change in aformat for an electron beam writer. Fracturing may include a dataoperation of, for example, scaling, data sizing, data rotation, patternreflection, color inversion, and/or the like. In transforming throughfracturing, data related to many systematic errors, which may occursomewhere during transferring from design data to an image on a wafer,may be corrected. The data correction of the systematic errors is calledmask process correction (MPC) and may include, for example, line widthadjustment, called CD adjustment, a work for increasing patternarrangement precision, and the like. Therefore, fracturing maycontribute to improvement of the quality of a final mask, and may alsobe a process performed in advance to correct a mask process. Herein, thesystematic errors may be caused by distortion occurring in an exposureprocess, a mask development and etching process, a wafer imagingprocess, and/or the like.

For example, the MDP may include the MPC. The MPC may indicate a processof correcting an error (e.g., a systematic error) occurring during anexposure process, as described above. Herein, the exposure process mayinclude electron beam writing, development, etching, baking, and/or thelike. In addition, data processing may be performed before the exposureprocess. The data processing is a kind of pre-processing on mask dataand may include grammar check on the mask data, exposure timeprediction, and the like.

After performing the MDP, a substrate for a mask is exposed to lightbased on the mask data in operation S296. Herein, the exposure mayindicate, for example, electron beam writing. Herein, the electron beamwriting may be performed by, for example, a gray writing scheme using amulti-beam mask writer (MBMW). Alternatively, the electron beam writingmay be performed using a variable shape beam (VSB) writer.

In addition, after performing the MDP, an operation of transforming themask data into pixel data before the exposure process may be performed.The pixel data may be data directly used for real exposure and mayinclude data about a shape to be exposed to light and data about a doseallocated to each of the data about the shape. Herein, the data aboutthe shape may be bit-map data transformed from shape data (e.g., vectordata), through, e.g., rasterization (and/or the like).

After the exposure process, a series of processes are performed tocomplete a mask. The series of processes may include, for example, adevelopment process, an etching process, a cleaning process, and thelike. In addition, the series of processes for mask manufacturing mayinclude a measurement process and a defect inspection and repairprocess. In addition, a pellicle coating process may be included.Herein, the pellicle coating process may indicate a process of attachingpellicles to protect the surface of a mask from possible contaminationduring mask delivery and a mask available life span after confirmingthrough final cleaning and inspection that there are no contaminationparticles and chemical stains.

The mask manufacturing method according to the present embodiment mayemploy the corner rounding method of a deep learning-based OPC patternin the OPC method. Particularly, operation S210 of generating a retargetlayout and/or operation S230 of generating an OPCed layout, in the OPCmethod, may employ the corner rounding method of a deep learning-basedOPC pattern. Accordingly, the mask manufacturing method according to thepresent embodiment may accurately generate a retarget layout and/or anOPCed layout based on the OPC method including the corner roundingmethod. As a result, the mask manufacturing method according to thepresent embodiment may manufacture a reliable mask based on an accurateOPCed layout.

As will be appreciated by one skilled in the art, the exampleembodiments in this disclosure may be embodied as a system, method,computer program product, and/or a computer program product embodied inone or more computer readable medium(s) having computer readable programcode embodied thereon. The computer readable program code may beprovided to a processor of a general purpose computer, special purposecomputer, and/or other programmable data processing apparatus. Thecomputer readable medium may be a computer readable signal medium and/ora computer readable storage medium. The computer readable storage mediummay be any tangible medium that can contain, and/or store a program foruse by or in connection with an instruction execution system, apparatus,or device. For example, the example embodiments may be implemented inprocessing circuitry such hardware, software, or the combination ofhardware and software. For example, the processing circuitry morespecifically may include (and/or be included in), but is not limited to,a processor, Central Processing Unit (CPU), a controller, an arithmeticlogic unit (ALU), a digital signal processor, a microcomputer, a fieldprogrammable gate array (FPGA), a System-on-Chip (SoC), a programmablelogic unit, a microprocessor, application-specific integrated circuit(ASIC), semiconductor elements in an integrated circuit, circuitsenrolled as an intellectual property (IP), etc. For example, the term“model” may refer to a software component and/or a hardware componentsuch as a field programmable gate array (FPGA) or an applicationspecific integrated circuit (ASIC), and/or combination of a hardwarecomponent and a software component. However, a “model” is not limited tosoftware or hardware. A “model” may be configured to be included in anaddressable storage medium or to reproduce one or more processors.Accordingly, for example, a “model” may include components such assoftware components, object-oriented software components, classcomponents, and task components, processes. functions, attributes,procedures, subroutines, segments of program code, drivers, firmware,microcode, circuits, data, databases, data structures, tables, arrays,and variables. A function provided in components or modules may beintegrated with a smaller number of components and/or divided intoadditional components.

The example embodiments may be applied to designing and manufacturingany electronic devices and systems. For example, the inventive conceptsmay be applied to systems such as a memory card, a solid state drive(SSD), an embedded multimedia card (eMMC), a universal flash storage(UFS), a mobile phone, a smart phone, a personal digital assistant(PDA), a portable multimedia player (PMP), a digital camera, acamcorder, a personal computer (PC), a server computer, a workstation, alaptop computer, a digital TV, a set-top box, a portable game console, anavigation system, a wearable device, an internet of things (IoT)device, an internet of everything (IoE) device, an e-book, a virtualreality (VR) device, an augmented reality (AR) device, a server system,an automotive driving system, etc.

While the inventive concepts have been particularly shown and describedwith reference to some example embodiments thereof, it will beunderstood that various changes in form and details may be made thereinwithout departing from the spirit and scope of the following claims.

1. A method of training a transform model to perform a corner roundingmethod of a deep learning-based optical proximity correction (OPC)pattern, the method comprising: obtaining a contour of at least one of aphotoresist (PR) pattern or an etching pattern on a wafer; obtaining asquare layout of the at least one PR pattern or etching pattern,corresponding to the contour; and generating a transform model throughdeep learning with the square layout and the contour such that thetransform model is configured to output a rounded layout target withrespect to a square layout target.
 2. The method of claim 1, wherein thedeep learning uses a deep convolutional generative adversarial network(DCGAN), an input of the DCGAN is the square layout, and an output ofthe DCGAN is the contour, and the generating the transform modelincludes training the transform model until the output contour is sameas a reference image corresponding to the contour.
 3. The method ofclaim 2, wherein the contour is obtained by using a mean value of aplurality of scanning electron microscope (SEM) images of the at leastone PR pattern or etching pattern.
 4. The method of claim 2, wherein thesquare layout includes square edges and is obtained by performingManhattanization on the contour.
 5. The method of claim 1, wherein thePR pattern corresponds to an after develop inspection (ADI) pattern, andthe etching pattern corresponds to an after clean inspection (ACI)pattern. 6.-8. (canceled)
 9. An optical proximity correction (OPC)method comprising: generating a retarget layout including straightedges; performing OPC on the retarget layout such that an OPCed layoutis generated; performing an optical rule check (ORC) on the OPCedlayout; determining whether there is a defect in the OPCed layout basedon a result of the ORC; determining the OPCed layout to be a final OPCedlayout based on the determination that there is no defect; and etching asemiconductor element based on the final OPCed layout, wherein at leastone of the retarget layout or the OPCed layout is generated based on acorner rounding of a deep learning-based OPC pattern.
 10. The OPC methodof claim 9, wherein the generating the retarget layout includes thecorner rounding of the deep learning-based OPC pattern and comprises:obtaining a first rounded layout target corresponding to a first squarelayout target by using a first transform model, and wherein the firsttransform model is generated by obtaining a first contour of an etchingpattern on a wafer, obtaining a first square layout of the etchingpattern, the first square layout corresponding to the first contour, andgenerating the first transform model through deep learning with thefirst square layout and the first contour such that the first transformmodel is configured to output the first rounded layout target withrespect to the first square layout target.
 11. The OPC method of claim10, wherein the deep learning uses a deep convolutional generativeadversarial network (DCGAN), an input of the DCGAN is the first squarelayout, and an output of the DCGAN is the first contour.
 12. The OPCmethod of claim 11, wherein the first contour is obtained using a meanvalue of scanning electron microscope (SEM) images of the etchingpattern on the wafer, and the first square layout includes square edgesand is obtained by performing Manhattanization on the first contour. 13.The OPC method of claim 10, wherein the etching pattern corresponds toan after clean inspection (ACI) pattern, and the generating the retargetlayout comprises performing an inverse correction on the first roundedlayout target such that a curvilinear after develop inspection (ADI)layout target is generated, and performing Manhattanization on thecurvilinear ADI layout target such that the retarget layout isgenerated.
 14. The OPC method of claim 9, wherein the generating theOPCed layout includes the corner rounding of the deep learning-based OPCpattern and comprises: obtaining a second rounded layout targetcorresponding to a second square layout target by using a secondtransform model, and wherein the second transform model is generated byobtaining a second contour of a photoresist (PR) pattern on a wafer,obtaining a second square layout of the PR pattern, the second squarelayout corresponding to the second contour, and generating the secondtransform model through deep learning with the second square layout andthe second contour such that the second transform model is configured tooutput the second rounded layout target with respect the second squarelayout target.
 15. The OPC method of claim 14, wherein the secondcontour is obtained using a mean value of scanning electron microscope(SEM) images of the PR pattern on the wafer, and the second squarelayout is obtained by performing Manhattanization on the second contourand includes square edges.
 16. The OPC method of claim 14, wherein thePR pattern corresponds to an after develop inspection (ADI) pattern, andthe generating the OPCed layout comprises performing an inversecorrection on the second rounded layout target.
 17. (canceled)
 18. Amask manufacturing method comprising: generating a retarget layoutincluding straight edges; performing an optical proximity correction(OPC) on the retarget layout such that an OPCed layout is generated;performing an optical rule check (ORC) on the OPCed layout; determiningwhether there is a defect in the OPCed layout based on a result of theORC; and determining the OPCed layout to be a final OPCed layout basedon the determination that there is no defect; transmitting an image ofthe final OPCed layout as mask tape-out (MTO) design data; preparingmask data based on the MTO design data; and exposing a substrate tolight, based on the mask data, wherein at least one of the retargetlayout or the OPCed layout is generated based on a corner rounding of adeep learning-based OPC pattern.
 19. The mask manufacturing method ofclaim 18, wherein the generating the retarget layout includes the cornerrounding of a deep learning-based OPC pattern and comprises: obtaining afirst rounded layout target corresponding to a first square layouttarget by using a first transform model, and wherein the first transformmodel is generated by obtaining a first contour of an etching pattern ona wafer, obtaining a first square layout of the etching pattern, thefirst square layout corresponding to the first contour, and generatingthe first transform model through deep learning with the first squarelayout and the first contour such that the first transform model isconfigured to output the first rounded layout target with respect to thefirst square layout target.
 20. The mask manufacturing method of claim19, wherein the deep learning uses a deep convolutional generativeadversarial network (DCGAN), an input of the DCGAN is the first squarelayout, and an output of the DCGAN is the first contour, the firstcontour is obtained using a mean value of scanning electron microscope(SEM) images of the etching pattern on the wafer, and the first squarelayout includes square edges and is obtained performing Manhattanizationon the first contour.
 21. The mask manufacturing method of claim 19,wherein the etching pattern corresponds to an after clean inspection(ACI) pattern, and the generating the retarget layout comprisesperforming an inverse correction on the first rounded layout target suchthat a curvilinear after develop inspection (ADI) layout target isgenerated, and performing Manhattanization on the curvilinear ADI layouttarget such that the retarget layout is generated.
 22. The maskmanufacturing method of claim 18, wherein the generating the OPCedlayout includes the corner rounding of the deep learning-based OPCpattern and comprises: obtaining a second rounded layout targetcorresponding to a second square layout target by using a secondtransform model, and wherein the second transform model is generated byobtaining a second contour of a photoresist (PR) pattern on a wafer,obtaining a second square layout of the PR pattern, the second squarelayout corresponding to the second contour, and generating the secondtransform model through deep learning with the second square layout andthe second contour such that the second transform model is configured tooutput the second rounded layout target with respect the second squarelayout target.
 23. The mask manufacturing method of claim 22, whereinthe second contour is obtained using a mean value of scanning electronmicroscope (SEM) images of the PR pattern on the wafer, and the secondsquare layout is obtained by performing Manhattanization on the secondcontour and includes square edges.
 24. The mask manufacturing method ofclaim 22, wherein the PR pattern corresponds to an after developinspection (ADI) pattern, and the generating the OPCed layout comprisesperforming an inverse correction on the second rounded layout target.